{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql #导入模块\n",
    "import pandas as pd\n",
    "\n",
    "#远程连接数据库\n",
    "db = pymysql.connect(\n",
    "         host='10.102.52.248',\n",
    "         port=3306,\n",
    "         user='root',\n",
    "         passwd='root',\n",
    "         db='casepro',\n",
    "         charset='utf8'\n",
    "         )\n",
    "def link_mysql(db):\n",
    "    #使用cursor()方法获取操作游标 \n",
    "    cursor = db.cursor()\n",
    "    sql = \"\"\"SELECT * FROM `t_plane_order`\"\"\"\n",
    "    sql1 = \"\"\"SELECT * FROM `t_plane_weather`\"\"\"\n",
    "    try:\n",
    "        cursor.execute(sql)  # 执行sql语句\n",
    "        result = cursor.fetchall()\n",
    "        columns = ['航班时刻表','航班号','子订单','日期','头等舱','公务舱','经济舱','其他','头等舱总数','公务舱总数','经济舱总数','其他总数']\n",
    "        frame = pd.DataFrame(list(result),columns=columns)\n",
    "        frame.to_csv(\"航班天气因素上座情况预测分析案例.csv\",encoding='utf-8')\n",
    "        cursor.execute(sql1)  # 执行sql语句\n",
    "        result1 = cursor.fetchall()\n",
    "        columns_weather = ['日期','高温','低温','天气状况','风','空气']\n",
    "        frame_weather = pd.DataFrame(list(result1),columns=columns_weather)\n",
    "        frame_weather.to_csv(\"天气数据.csv\",encoding='utf-8')\n",
    "    except Exception:\n",
    "        db.rollback()  # 发生错误时回滚\n",
    "        print(\"查询失败\")\n",
    "    cursor.close()  # 关闭游标\n",
    "    db.close()  # 关闭数据库连接\n",
    "link_mysql(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#对航班数据进行清理\n",
    "def yucli_plane():\n",
    "    df_plane = pd.read_csv('航班天气因素上座情况预测分析案例.csv',encoding='utf-8')\n",
    "    df_plane = pd.DataFrame(data=df_plane)\n",
    "    df_plane = df_plane.drop(columns='Unnamed: 0')\n",
    "    #判断其空占比\n",
    "    df_plane_percent = df_plane.isna().sum().sort_values(ascending=False) / len(df_plane)\n",
    "    print(\"各字段的空占比为：\\n{}\".format(df_plane_percent))\n",
    "    #删除空数据\n",
    "    df_plane_notNu = df_plane.dropna(axis=0)\n",
    "    #删除重复数据\n",
    "    df_plane_notNu = df_plane_notNu.drop_duplicates()\n",
    "    #将str类型改为float\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i] = df_plane_notNu.iloc[:,i].astype(float)\n",
    "    #将负值替换成零\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i][df_plane_notNu.iloc[:,i] < 0] = 0.0\n",
    "    #删除日期中不规范的数据\n",
    "    df_plane_notNu[df_plane_notNu.iloc[:,3] == \"0.0\"] = np.nan\n",
    "    df_plane_notNu.dropna(axis=0)\n",
    "    return df_plane_notNu\n",
    "\n",
    "#对天气数据进行处理\n",
    "def yucli_weath():\n",
    "    df_weath = pd.read_csv('天气数据.csv',encoding='utf-8')\n",
    "    df_weath = pd.DataFrame(df_weath)\n",
    "    #删除无分析价值数据\n",
    "    df_weath = df_weath.drop('Unnamed: 0',axis=1)\n",
    "    #将日期和空气格式化\n",
    "    df_weath['日期'] = df_weath['日期'].apply(lambda x:str(x)[0:10])\n",
    "    df_weath['空气'] = df_weath['空气'].apply(lambda x:str(x)[:-1])\n",
    "    return df_weath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3249: DtypeWarning: Columns (3,5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各字段的空占比为：\n",
      "子订单      0.272959\n",
      "航班号      0.013529\n",
      "日期       0.000389\n",
      "其他总数     0.000000\n",
      "经济舱总数    0.000000\n",
      "公务舱总数    0.000000\n",
      "头等舱总数    0.000000\n",
      "其他       0.000000\n",
      "经济舱      0.000000\n",
      "公务舱      0.000000\n",
      "头等舱      0.000000\n",
      "航班时刻表    0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>航班时刻表</th>\n",
       "      <th>头等舱</th>\n",
       "      <th>公务舱</th>\n",
       "      <th>经济舱</th>\n",
       "      <th>其他</th>\n",
       "      <th>头等舱总数</th>\n",
       "      <th>公务舱总数</th>\n",
       "      <th>经济舱总数</th>\n",
       "      <th>其他总数</th>\n",
       "      <th>高温</th>\n",
       "      <th>低温</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th>航班号</th>\n",
       "      <th>天气状况</th>\n",
       "      <th>风</th>\n",
       "      <th>空气</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">2016-01-02</td>\n",
       "      <td>8160</td>\n",
       "      <td>霾~雾</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>1615205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9581</td>\n",
       "      <td>霾~雾</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>2297904.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BR715</td>\n",
       "      <td>霾~雾</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>4525245.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CA101</td>\n",
       "      <td>霾~雾</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>400218.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CA107</td>\n",
       "      <td>霾~雾</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>404502.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">2016-12-31</td>\n",
       "      <td>ZH9157</td>\n",
       "      <td>霾</td>\n",
       "      <td>南风1-2级</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>3144374.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZH9159</td>\n",
       "      <td>霾</td>\n",
       "      <td>南风1-2级</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>3949220.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZH9163</td>\n",
       "      <td>霾</td>\n",
       "      <td>南风1-2级</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>3068798.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZH9165</td>\n",
       "      <td>霾</td>\n",
       "      <td>南风1-2级</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>3072106.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZH9168</td>\n",
       "      <td>霾</td>\n",
       "      <td>南风1-2级</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>3083458.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200655 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         航班时刻表  头等舱  公务舱   经济舱   其他  头等舱总数  \\\n",
       "日期         航班号    天气状况 风       空气                                            \n",
       "2016-01-02 8160   霾~雾  无持续风向微风 严重污染  1615205.0  0.0  0.0   0.0  0.0   16.0   \n",
       "           9581   霾~雾  无持续风向微风 严重污染  2297904.0  0.0  0.0  15.0  0.0   24.0   \n",
       "           BR715  霾~雾  无持续风向微风 严重污染  4525245.0  0.0  0.0   8.0  3.0    0.0   \n",
       "           CA101  霾~雾  无持续风向微风 严重污染   400218.0  0.0  0.0   0.0  0.0   48.0   \n",
       "           CA107  霾~雾  无持续风向微风 严重污染   404502.0  0.0  0.0   2.0  0.0   32.0   \n",
       "...                                        ...  ...  ...   ...  ...    ...   \n",
       "2016-12-31 ZH9157 霾    南风1-2级  重度污染  3144374.0  0.0  0.0  15.0  0.0   24.0   \n",
       "           ZH9159 霾    南风1-2级  重度污染  3949220.0  0.0  0.0   0.0  0.0   24.0   \n",
       "           ZH9163 霾    南风1-2级  重度污染  3068798.0  3.0  0.0  45.0  0.0   24.0   \n",
       "           ZH9165 霾    南风1-2级  重度污染  3072106.0  0.0  0.0  10.0  0.0   24.0   \n",
       "           ZH9168 霾    南风1-2级  重度污染  3083458.0  0.0  0.0  15.0  0.0    0.0   \n",
       "\n",
       "                                     公务舱总数   经济舱总数   其他总数    高温    低温  \n",
       "日期         航班号    天气状况 风       空气                                      \n",
       "2016-01-02 8160   霾~雾  无持续风向微风 严重污染    0.0   324.0    0.0  10.0  -8.0  \n",
       "           9581   霾~雾  无持续风向微风 严重污染    0.0   450.0    0.0  15.0 -12.0  \n",
       "           BR715  霾~雾  无持续风向微风 严重污染  195.0  1190.0  280.0  25.0 -20.0  \n",
       "           CA101  霾~雾  无持续风向微风 严重污染    0.0   483.0    0.0  15.0 -12.0  \n",
       "           CA107  霾~雾  无持续风向微风 严重污染    0.0   322.0    0.0  15.0 -12.0  \n",
       "...                                    ...     ...    ...   ...   ...  \n",
       "2016-12-31 ZH9157 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9159 霾    南风1-2级  重度污染    0.0   450.0    0.0  20.0 -25.0  \n",
       "           ZH9163 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9165 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9168 霾    南风1-2级  重度污染   24.0   480.0    0.0  16.0 -20.0  \n",
       "\n",
       "[200655 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = pd.merge(left=yucli_plane(), right=yucli_weath(), on=\"日期\", how=\"inner\")\n",
    "df_result.groupby(['日期','航班号','天气状况','风','空气']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           头等舱      公务舱       经济舱\n",
      "空气                               \n",
      "严重污染    8945.0   4612.0   71030.0\n",
      "中度污染   25970.0  14072.0  177521.0\n",
      "优     113848.0  59397.0  737457.0\n",
      "良      92618.0  48086.0  586591.0\n",
      "轻度污染   61623.0  30267.0  414415.0\n",
      "重度污染   23027.0  12151.0  156046.0\n",
      "['严重污染' '中度污染' '优' '良' '轻度污染' '重度污染']\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按空气聚合'头等舱','公务舱','经济舱'\n",
    "df_air_cabin=df_result.groupby('空气')['头等舱','公务舱','经济舱'].sum()\n",
    "print(df_air_cabin)\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "x=df_air_cabin.index.values\n",
    "print(x)\n",
    "y1=df_air_cabin[['头等舱']]\n",
    "y2=df_air_cabin[['公务舱']]\n",
    "y3=df_air_cabin[['经济舱']]\n",
    "#画图\n",
    "plt.plot(x,y1,marker='p')\n",
    "plt.plot(x,y2,marker='p')\n",
    "plt.plot(x,y3,marker='p')\n",
    "#plt.xticks(x,\"\")\n",
    "plt.xlabel(\"空气\")\n",
    "plt.ylabel(\"上座数\")\n",
    "plt.title(\"航班的头等舱、公务舱、经济舱在不同空气等级下的购买数\")\n",
    "#设置数字标签\n",
    "#头等舱\n",
    "for a, b in zip(x, y1.values):\n",
    "    plt.text(a, b, b, ha='center', va='bottom', fontsize=9,rotation=20)\n",
    "#经济舱\n",
    "for a, b in zip(x, y3.values):\n",
    "    plt.text(a, b, b, ha='center', va='bottom', fontsize=9,rotation=5)\n",
    "#公务舱\n",
    "for a, b in zip(x, y2.values):\n",
    "    plt.text(a, b, b, ha='center', va='top', fontsize=9)   \n",
    "tuli=['头等舱','公务舱','经济舱']\n",
    "plt.legend(tuli)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          头等舱总数      公务舱总数       经济舱总数\n",
      "空气                                    \n",
      "严重污染   186068.0   127776.0   2895117.0\n",
      "中度污染   536201.0   363157.0   8316388.0\n",
      "优     2326438.0  1604805.0  36312589.0\n",
      "良     1900952.0  1313111.0  29635403.0\n",
      "轻度污染  1227025.0   848090.0  19194221.0\n",
      "重度污染   489564.0   356279.0   7854536.0\n",
      "[ 3208961.  9215746. 40243832. 32849466. 21269336.  8700379.]\n"
     ]
    }
   ],
   "source": [
    "#空气——'头等舱总数','公务舱总数','经济舱总数'\n",
    "df_air_cabin_sum=df_result.groupby('空气')['头等舱总数','公务舱总数','经济舱总数'].sum()\n",
    "print(df_air_cabin_sum)\n",
    "\n",
    "#'头等舱'+'公务舱'+'经济舱'=\"购票数\"\n",
    "book=df_air_cabin[\"头等舱\"].values+df_air_cabin[\"公务舱\"].values+df_air_cabin[\"经济舱\"].values\n",
    "#'头等舱总数'+'公务舱总数'+'经济舱总数'='上座位'\n",
    "cabin_sum=df_air_cabin_sum[\"头等舱总数\"].values+df_air_cabin_sum[\"公务舱总数\"].values+df_air_cabin_sum[\"经济舱总数\"].values\n",
    "print(cabin_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "空气\n",
      "严重污染     186068.0\n",
      "中度污染     536201.0\n",
      "优       2326438.0\n",
      "良       1900952.0\n",
      "轻度污染    1227025.0\n",
      "重度污染     489564.0\n",
      "Name: 头等舱总数, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "plt.subplot(2,2,1)\n",
    "seat_x=df_air_cabin_sum.index\n",
    "seat_y=df_air_cabin_sum['头等舱总数']\n",
    "plt.pie(seat_y,labels=seat_x)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "book_x=df_air_cabin.index\n",
    "book_y=df_air_cabin[\"头等舱\"]\n",
    "print(seat_y)\n",
    "#plt.bar(seat_x,seat_y)\n",
    "plt.pie(book_y,labels=book_x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "空气\n",
      "严重污染    20.80\n",
      "中度污染    20.65\n",
      "优       20.43\n",
      "良       20.52\n",
      "轻度污染    19.91\n",
      "重度污染    21.26\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27c808ef588>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#上座率\n",
    "seat_ratio=seat_y/book_y\n",
    "#seat_ratio=book_y/seat_y\n",
    "#只保留小数点后两位的数据\n",
    "seat_ratio=round(seat_ratio,2)\n",
    "print(seat_ratio)\n",
    "plt.xlabel(\"空气等级\")\n",
    "plt.ylabel(\"上座率\")\n",
    "plt.title(\"航班的头等舱、公务舱、经济舱在不同空气等级下的上座率\")\n",
    "for a, b in zip(seat_x, seat_ratio):\n",
    "    plt.text(a, b, b, ha='center', va='bottom', fontsize=9,rotation=20)\n",
    "plt.legend(\"上座率\")\n",
    "plt.plot(seat_x,seat_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
